As the ongoing COVID-19 outbreak remains a global threat, it is a challenge for all the countries to come up with effective public health and administrative strategies to battle against COVID-19 and sustain their economies.In this research, we are interested in predicting the epidemic peak outbreak of the Coronavirus in South Africa, Turkey, and Brazil. Until now, there is no known safe treatment, hence the immunity system of the individual has a crucial role in recovering from this contagious disease. In general, the aged individuals probably have the highest rate of mortality due to COVID-19. It is well known that this immunity system can be affected by the age of the individual, so it is wise to consider an age-structured SEIR system to model Coronavirus transmission. For the COVID-19 epidemic, the individuals in the incubation stage are capable of infecting the susceptible individuals. All the mentioned points are regarded in building the responsible predictive mathematical model. The investigated model allows us to predict the spread of COID-19 in South Africa, Turkey, and Brazil. The epidemic peak outbreak in these countries is considered, and the estimated time of the end of infection is regarded by the help of some numerical simulations. Further, the influence of the isolation of the infected persons on the spread of COVID-19 disease is investigated.Corona virus disease (COVID-19) is an extremely serious infection with an extremely high death rate worldwide. In March, the disease was declared a „global pandemic” by the World Health Organization (WHO). Until now, there is no known vaccine or drug, since the unknown things related to the disease are more important than our theoretical and empirical knowledge. However, mathematical modeling and the estimation of the basic number of reproductions can provide clarifications in order to determine the potential and severity of this epidemic and therefore provide essential information to identify the type of measures and interventions to be taken to control the intensity of the spread of the disease. Hence, in this paper, we propose a new deterministic compartmental model based on the clinical progression of the disease, the epidemiological state of the individuals and the intervention for the dynamics of COVID-19 infections. Our approach consists of seven phenotypes the susceptible humans, exposed humans, infectious humans, the recovered humans, the quarantine population, there recovered-exposed and deceased population. We proved first through mathematical approach the positivity, boundness and existence of a solution to the considered model. We also studied the existence of the disease free equilibrium and corresponding stability. Our work shows, in particular, that the disease will decrease if the number of reproduction R 0 was less than one. Moreover, the impact of the quarantine strategies to reduce the spread of this disease is discussed. The theoretical results are validated by some numerical simulations of the system of the epidemic’s differential equations. It should be mentioned that, the error between the considered model and the official data curve is quite small.In this article, a mathematical model for the transmission of COVID-19 disease is formulated and analysed. It is shown that the model exhibits a backward bifurcation at R 0 = 1 when recovered individuals do not develop a permanent immunity for the disease. In the absence of reinfection, it is proved that the model is without backward bifurcation and the disease free equilibrium is globally asymptotically stable for R 0 less then 1 . By using available data, the model is validated and parameter values are estimated. The sensitivity of the value of R 0 to changes in any of the parameter values involved in its formula is analysed. Moreover, various mitigation strategies are investigated using the proposed model and it is observed that the asymptomatic infectious group of individuals may play the major role in the re-emergence of the disease in the future. Therefore, it is recommended that in the absence of vaccination, countries need to develop capacities to detect and isolate at least 30% of the asymptomatic infectious group of individuals while treating in isolation at least 50% of symptomatic patients to control the disease.Presently, COVID-19 has posed a serious threat to researchers, scientists, health professionals, and administrations around the globe from its detection to its treatment. The whole world is witnessing a lockdown like situation because of COVID-19 pandemic. Persistent efforts are being made by the researchers to obtain the possible solutions to control this pandemic in their respective areas. One of the most common and effective methods applied by the researchers is the use of CT-Scans and X-rays to analyze the images of lungs for COVID-19. However, it requires several radiology specialists and time to manually inspect each report which is one of the challenging tasks in a pandemic. In this paper, we have proposed a deep learning neural network-based method nCOVnet, an alternative fast screening method that can be used for detecting the COVID-19 by analyzing the X-rays of patients which will look for visual indicators found in the chest radiography imaging of COVID-19 patients.Coronaviruses are highly transmissible and are pathogenic viruses of the 21st century worldwide. In general, these viruses are originated in bats or rodents. At the same time, the transmission of the infection to the human host is caused by domestic animals that represent in the habitat the intermediate host. In this study, we review the currently collected information about coronaviruses and establish a model of differential equations with piecewise constant arguments to discuss the spread of the infection from the natural host to the intermediate, and from them to the human host, while we focus on the potential spillover of bat-borne coronaviruses. The local stability of the positive equilibrium point of the model is considered via the Linearized Stability Theorem. Besides, we discuss global stability by employing an appropriate Lyapunov function. To analyze the outbreak in early detection, we incorporate the Allee effect at time t and obtain stability conditions for the dynamical behavior. Furthermore, it is shown that the model demonstrates the Neimark-Sacker Bifurcation. Finally, we conduct numerical simulations to support the theoretical findings.COVID-19 remains a major pandemic currently threatening all the countries of the world. In Nigeria, there were 1, 932 COVID-19 confirmed cases, 319 discharged cases and 58 deaths as of 30th April 2020. This paper, therefore, subjected the daily cumulative reported COVID-19 cases of these three variables to nine (9) curve estimation statistical models in simple, quadratic, cubic, and quartic forms. It further identified the best of the thirty-six (36) models and used the same for prediction and forecasting purposes. The data collected by the Nigeria Centre for Disease Control for sixty-four (64) days, two (2) months and three (3), were daily monitored and eventually analyzed. We identified the best models to be Quartic Linear Regression Model with an autocorrelated error of order 1 (AR(1)); and found the Ordinary Least Squares, Cochrane Orcutt, Hildreth-Lu, and Prais-Winsten and Least Absolute Deviation (LAD) estimators useful to estimate the models’ parameters. Consequently, we recommended the daily cumulative forecast values of the LAD estimator for May and June 2020 with a 99% confidence level. The forecast values are alarming, and so, the Nigerian Government needs to hastily review her activities and interventions towards COVID-19 to provide some tactical and robust structures and measures to avert these challenges.During the recent Covid-19 pandemic, additive Technology and Social Media were used to tackle the shortage of Personal Protective Equipment. A literature review and a social media listening software were employed to explore the number of the users referring to specific keywords related to 3D printing and PPE. Additionally, the QALY model was recruited to highlight the importance of the PPE usage. More than 7 billion users used the keyword covid or similar in the web while mainly Twitter and Facebook were used as a world platform for PPE designs distribution through individuals and more than 100 different 3D printable PPE designs were developed.At the beginning of 2020, the spread of a new strand of Coronavirus named SARS-CoV-2 (COVID-19) raised the interest of the scientific community about the risk assessment related to the viral infection. The contagion became pandemic in few months forcing many Countries to declare lockdown status. In this context of quarantine, all commercial and productive activities are suspended, and many Countries are experiencing a serious crisis. To this aim, the understanding of risk of contagion in every urban district is fundamental for governments and administrations to establish reopening strategies. This paper proposes the calibration of an index able to predict the risk of contagion in urban districts in order to support the administrations in identifying the best strategies to reduce or restart the local activities during lockdown conditions. The objective regards the achievement of a useful tool to predict the risk of contagion by considering socio-economic data such as the presence of activities, companies, institutions and number of infections in urban districts. The proposed index is based on a factorial formula, simple and easy to be applied by practitioners, calibrated by using an optimization-based procedure and exploiting data of 257 urban districts of Apulian region (Italy). Moreover, a comparison with a more refined analysis, based on the training of Artificial Neural Networks, is performed in order to take into account the non-linearity of the phenomenon. The investigation quantifies the influence of each considered parameter in the risk of contagion useful to obtain risk analysis and forecast scenarios.At the core of ecological economics is the image of the economy as an open system embedded in the natural environment whose carrying capacity is limited. The present paper revisits this image by drawing upon the constructivist implications of Luhmann’s social systems theory. To Luhmann, the modern society consists of a multitude of social systems each bringing forth and observing their own environments. If the Luhmannian vision is accepted, then ecological economics can be said to privilege the observational perspective of natural sciences. The unfortunate consequence of this privileging is the underestimation of a broad range of multidimensional sustainability risks which are foregrounded by the numerous alternative observational perspectives which are just as legitimate. It is argued that, rather than relativizing the sustainability concerns of the modern ecological economics, the Luhmannian perspective generalizes and radicalizes them. In doing so, the latter perspective opens new possibilities not only for navigating these risks but also for envisioning new resources and solutions.Thin-film formation and transport properties of two copper-paddlewheel metal-organic framework (MOF) -based systems (MOF-14 and MOF-399) are investigated for their potential integration into electrochemical device architectures. Thin-film analogs of these two systems are fabricated by the sequential, alternating, solution-phase deposition of the inorganic and organic ligand precursors that result in conformal films via van der Merwe-like growth. Atomic force microscopy reveals smooth film morphologies with surface roughnesses determined by the underlying substrates and linear film growth of 1.4 and 2.2 nm per layer for the MOF-14 and MOF-399 systems, respectively. Electrochemical impedance spectroscopy is used to evaluate the electronic transport properties of the thin films, finding that the MOF-14 analog films demonstrate low electronic conductivity, while MOF-399 analog films are electronically insulating. The intrinsic porosities of these ultrathin MOF analog films are confirmed by cyclic voltammetry redox probe characterization using ferrocene. Larger peak currents are observed for MOF-399 analog films compared to MOF-14 analog films, which is consistent with the larger pores of MOF-399. The layer-by-layer deposition of these systems provides a promising route to incorporate MOFs as thin films with nanoscale thickness control and low surface roughness for electrochemical devices.The Social Support for Exercise Subscales are commonly used among Hispanic populations. The aims of this study were to test the validity and reliability of the Spanish-language version of the Social Support for Exercise Subscales, and test the invariance of the Spanish- and English-language versions. Data were from a subsample of Hispanic adults in the Cameron County Hispanic Cohort (n=1,447). A series of confirmatory factor analysis (CFA) models were used to assess the validity and reliability of the Spanish-language version of the subscales. A multi group CFA approach was used to test measurement invariance. Results indicated the Spanish-language versions of family and friend support subscales had good validity and reliability (RMSEA0.94, and SRMR less then 0.05). There was also evidence of measurement invariance between the Spanish- and English-language versions. These findings indicate the Spanish-language family and friend support subscales are valid and can be compared between Spanish- and English-language Hispanic respondents.Very first thing, I hope this finds each of you, your families, and friends healthy. These are difficult times for all of us and there are priorities much greater than our professions at the moment. For those of you facing hardship, we extend our heartfelt sympathies and wish your circumstances to improve soon.This paper presents the formulation and application of a novel agent-based integrated assessment approach to model the attributes, objectives and decision-making process of investors in a long-term energy transition in India’s iron and steel sector. It takes empirical data from an on-site survey of 108 operating plants in Maharashtra to formulate objectives and decision-making metrics for the agent-based model and simulates possible future portfolio mixes. The studied decision drivers were capital costs, operating costs (including fuel consumption), a combination of capital and operating costs, and net present value. Where investors used a weighted combination of capital cost and operating costs, a natural gas uptake of ~12PJ was obtained and the highest cumulative emissions reduction was obtained, 2 Mt CO2 in the period from 2020 to 2050. Conversely if net present value alone is used, cumulative emissions reduction in the same period was lower, 1.6 Mt CO2, and the cumulative uptake of natural gas was equal to 15PJ. Results show how the differing upfront investment cost of the technology options could cause prevalence of high-carbon fuels, particularly heavy fuel oil, in the final mix. Results also represent the unique heterogeneity of fuel-switching industrial investors with distinct investment goals and limited foresight on costs. The perception of high capital expenditures for decarbonisation represents a significant barrier to the energy transition in industry and should be addressed via effective policy making (e.g. carbon policy/price).Carbon mitigation strategies are an urgent and overdue tourism industry imperative. The tourism response to climate action has been to engage businesses in technology adoption, and to encourage more sustainable visitor behaviour. These strategies however are insufficient to mitigate the soaring carbon footprint of tourism. Building upon the concepts of optimization and eco-efficiency, we put forward a novel carbon mitigation approach, which seeks to pro-actively determine, foster, and develop a long-term tourist market portfolio. This can be achieved through intervening and reconfiguring the demand mix with the fundamental aim of promoting low carbon travel markets. The concept and the analytical framework that quantitatively inform optimization of the desired market mix are presented. Combining the „de-growth” and „optimization” strategies, it is demonstrated that in the case study of Taiwan, great potential exists to reduce emissions and sustain economic yields. The implications for tourism destination managers and wider industry stakeholders are discussed.The current global health emergency triggered by the pandemic COVID-19 is one of the greatest challenges we face in this generation. Computational simulations have played an important role to predict the development of the current pandemic. Such simulations enable early indications on the future projections of the pandemic and is useful to estimate the efficiency of control action in the battle against the SARS-CoV-2 virus. The SEIR model is a well-known method used in computational simulations of infectious viral diseases and it has been widely used to model other epidemics such as Ebola, SARS, MERS, and influenza A. This paper presents a modified SEIRS model with additional exit conditions in the form of death rates and resusceptibility, where we can tune the exit conditions in the model to extend prediction on the current projections of the pandemic into three possible outcomes; death, recovery, and recovery with a possibility of resusceptibility. The model also considers specific information such as ageing factor of the population, time delay on the development of the pandemic due to control action measures, as well as resusceptibility with temporal immune response. Owing to huge variations in clinical symptoms exhibited by COVID-19, the proposed model aims to reflect better on the current scenario and case data reported, such that the spread of the disease and the efficiency of the control action taken can be better understood. The model is verified using two case studies based on the real-world data in South Korea and Northern Ireland.Artificial intelligence (AI) has proven to be superior to human decision-making in certain areas. This is particularly the case whenever there is a need for advanced strategic reasoning and analysis of vast amounts of data in order to solve complex problems. Few human activities fit this description better than politics. In politics we deal with some of the most complex issues humans face, short-term and long-term consequences have to be balanced, and we make decisions knowing that we do not fully understand their consequences. I examine an extreme case of the application of AI in the domain of government, and use this case to examine a subset of the potential harms associated with algorithmic governance. I focus on five objections based on political theoretical considerations and the potential political harms of an AI technocracy. These are objections based on the ideas of 'political man’ and participation as a prerequisite for legitimacy, the non-morality of machines and the value of transparency and accountability. I conclude that these objections do not successfully derail AI technocracy, if we make sure that mechanisms for control and backup are in place, and if we design a system in which humans have control over the direction and fundamental goals of society. Such a technocracy, if the AI capabilities of policy formation here assumed becomes reality, may, in theory, provide us with better means of participation, legitimacy, and more efficient government.The COVID-19 outbreak is a sharp reminder that pandemics, like other rarely occurring catastrophes, have happened in the past and will continue to happen in the future. Even if we cannot prevent dangerous viruses from emerging, we should prepare to dampen their effects on society. The current outbreak has had severe economic consequences across the globe, and it does not look like any country will be unaffected. This not only has consequences for the economy; all of society is affected, which has led to dramatic changes in how businesses act and consumers behave. This special issue is a global effort to address some of the pandemic-related issues affecting society. In total, there are 13 papers that cover different industry sectors (e.g., tourism, retail, higher education), changes in consumer behavior and businesses, ethical issues, and aspects related to employees and leadership.The COVID-19 pandemic and the lockdown and social distancing mandates have disrupted the consumer habits of buying as well as shopping. Consumers are learning to improvise and learn new habits. For example, consumers cannot go to the store, so the store comes to home. While consumers go back to old habits, it is likely that they will be modified by new regulations and procedures in the way consumers shop and buy products and services. New habits will also emerge by technology advances, changing demographics and innovative ways consumers have learned to cope with blurring the work, leisure, and education boundaries.This essay applies the „ultimate broadening of the concept of marketing” for designing and implementing interventions in public laws and policy, national and local regulations, and everyday lives of individuals. The ultimate broadening of the concept of marketing Marketing is any activity, message, emotion, or behavior by someone, firm, organization, government, community, or brand executed consciously or nonconsciously that may stimulate an observable or non-observable activity, emotion, attitude, belief, or thought by someone else, group, organization, firm or community. The broadening definition applies to the current interventions by national and state/provincial governments as well as healthcare facilities, medical science facilities, firms, and individuals to mitigate and eliminate the impact of the COVID-19 pandemic. Framing interventions as experiments is helpful in improving the quality of their designs, implementing them successfully, and validly interpreting their effectiveness. In January and February 2020, a few nations were exemplars for accurately forecasting the coming disaster of COVID-19 as a cause of illness and death and in designing/implementing effective mitigating strategies Denmark, Finland, Republic of Korea, New Zealand, Norway, and Vietnam. While the COVID-19 prevention intervention tests now being run for several promising vaccines are true experiments, the researchers analyzing the data from these interventions may need prompting to examine the efficacy of each vaccine tested by modeling demographic subgroups for the members in the treatment and placebo groups in the randomized control trials.InDisc is an R Package that implements procedures for estimating and fitting unidimensional Item Response Theory (IRT) Dual Models (DMs). DMs are intended for personality and attitude measures and are, essentially, extended standard IRT models with an extra person parameter that models the discriminating power of the individual. The package consists of a main function, which calls subfunctions for fitting binary, graded, and continuous responses. The program, a detailed user’s guide, and an empirical example are available at no cost to the interested practitioner.Accurate item calibration in models of item response theory (IRT) requires rather large samples. For instance, N > 500 respondents are typically recommended for the two-parameter logistic (2PL) model. Hence, this model is considered a large-scale application, and its use in small-sample contexts is limited. Hierarchical Bayesian approaches are frequently proposed to reduce the sample size requirements of the 2PL. This study compared the small-sample performance of an optimized Bayesian hierarchical 2PL (H2PL) model to its standard inverse Wishart specification, its nonhierarchical counterpart, and both unweighted and weighted least squares estimators (ULSMV and WLSMV) in terms of sampling efficiency and accuracy of estimation of the item parameters and their variance components. To alleviate shortcomings of hierarchical models, the optimized H2PL (a) was reparametrized to simplify the sampling process, (b) a strategy was used to separate item parameter covariances and their variance components, and (c) the variance components were given Cauchy and exponential hyperprior distributions. Results show that when combining these elements in the optimized H2PL, accurate item parameter estimates and trait scores are obtained even in sample sizes as small as N = 100 . This indicates that the 2PL can also be applied to smaller sample sizes encountered in practice. The results of this study are discussed in the context of a recently proposed multiple imputation method to account for item calibration error in trait estimation.Item parameter estimates of a common item on a new test form may change abnormally due to reasons such as item overexposure or change of curriculum. A common item, whose change does not fit the pattern implied by the normally behaved common items, is defined as an outlier. Although improving equating accuracy, detecting and eliminating of outliers may cause a content imbalance among common items. Robust scale transformation methods have recently been proposed to solve this problem when only one outlier is present in the data, although it is not uncommon to see multiple outliers in practice. In this simulation study, the authors examined the robust scale transformation methods under conditions where there were multiple outlying common items. Results indicated that the robust scale transformation methods could reduce the influences of multiple outliers on scale transformation and equating. The robust methods performed similarly to a traditional outlier detection and elimination method in terms of reducing the influence of outliers while keeping adequate content balance.This study examined whether cutoffs in fit indices suggested for traditional formats with maximum likelihood estimators can be utilized to assess model fit and to test measurement invariance when a multiple group confirmatory factor analysis was employed for the Thurstonian item response theory (IRT) model. Regarding the performance of the evaluation criteria, detection of measurement non-invariance and Type I error rates were examined. The impact of measurement non-invariance on estimated scores in the Thurstonian IRT model was also examined through accuracy and efficiency in score estimation. The fit indices used for the evaluation of model fit performed well. Among six cutoffs for changes in model fit indices, only ΔCFI > .01 and ΔNCI > .02 detected metric non-invariance when the medium magnitude of non-invariance occurred and none of the cutoffs performed well to detect scalar non-invariance. Based on the generated sampling distributions of fit index differences, this study suggested ΔCFI > .001 and ΔNCI > .004 for scalar non-invariance and ΔCFI > .007 for metric non-invariance. Considering Type I error rate control and detection rates of measurement non-invariance, ΔCFI was recommended for measurement non-invariance tests for forced-choice format data. Challenges in measurement non-invariance tests in the Thurstonian IRT model were discussed along with the direction for future research to enhance the utility of forced-choice formats in test development for cross-cultural and international settings.Cognitive diagnostic models (CDMs) are of growing interest in educational research because of the models’ ability to provide diagnostic information regarding examinees’ strengths and weaknesses suited to a variety of content areas. An important step to ensure appropriate uses and interpretations from CDMs is to understand the impact of differential item functioning (DIF). While methods of detecting DIF in CDMs have been identified, there is a limited understanding of the extent to which DIF affects classification accuracy. This simulation study provides a reference to practitioners to understand how different magnitudes and types of DIF interact with CDM item types and group distributions and sample sizes to influence attribute- and profile-level classification accuracy. The results suggest that attribute-level classification accuracy is robust to DIF of large magnitudes in most conditions, while profile-level classification accuracy is negatively influenced by the inclusion of DIF. Conditions of unequal group distributions and DIF located on simple structure items had the greatest effect in decreasing classification accuracy. The article closes by considering implications of the results and future directions.Personalized recommendation system has been widely adopted in E-learning field that is adaptive to each learner’s own learning pace. With full utilization of learning behavior data, psychometric assessment models keep track of the learner’s proficiency on knowledge points, and then, the well-designed recommendation strategy selects a sequence of actions to meet the objective of maximizing learner’s learning efficiency. This article proposes a novel adaptive recommendation strategy under the framework of reinforcement learning. The proposed strategy is realized by the deep Q-learning algorithms, which are the techniques that contributed to the success of AlphaGo Zero to achieve the super-human level in playing the game of go. The proposed algorithm incorporates an early stopping to account for the possibility that learners may choose to stop learning. It can properly deal with missing data and can handle more individual-specific features for better recommendations. The recommendation strategy guides individual learners with efficient learning paths that vary from person to person. The authors showcase concrete examples with numeric analysis of substantive learning scenarios to further demonstrate the power of the proposed method.This study investigated the impact of cooking fuel choice on the health of elderly people, as measured by activities of daily living, using micro survey data from the China Health and Retirement Longitudinal Study 2015. In contrast to previous studies, our focus on activities of daily living allows for a more comprehensive analysis of health outcomes than diagnoses or doctor visits. Propensity score matching and an endogenous switching regression model were used to address potential selection biases. We found a strong and positive effect of using non-solid cooking fuels on an individual’s ability to cope with daily activities, with substantially greater effects on female and older respondents. Our results highlight the need to support energy transition in rural households to non-solid fuels for cooking. We also discuss potential policies to facilitate energy transition in rural China.Squamocin, an annonaceous acetogenin has been experimentally isolated and characterized in the solid state using the FT-IR and FT-Raman spectra and in methanol solution by UV-visible spectrum. The main bands observed were assigned combining the IR and Raman spectra with hybrid functional B3LYP/6-31G∗ calculations. Structural, electronic and topological properties were predicted at the same level of theory for the most stable conformer of squamocin in gas phase and methanol solution. A corrected solvation energy value of -147.54 kJ/mol was predicted for squamocin in methanol while the atomic population natural (NPA) charges evidence higher values on O atoms of R2 and R3 rings, as compared with the corresponding to lactone ring. Mapped MEP surfaces suggest that nucleophilic regions are located on the O atoms of three rings and of OH bonds belonging to side chain, in agreement with the higher charges values evidenced on these O atoms while electrophilic regions are predicted on the H atoms of OH groups. High stabilities of squamocin in both media was revealed by AIM studies while only in methanol solution by NBO calculations. The expansion of volume and the higher dipole moment in methanol suggest a clear solvation of squamocin by solvent molecules. Gap values have evidenced that squamocin is most reactive in methanol while that its large aliphatic chain produces an increases the reactivity of this γ-lactone, as compared with ascorbic acid lactone. Reasonable concordances among the predicted UV-visible and IR, Raman spectra with the corresponding experimental ones were found.Compounds with dihydroquinoline-4(1H)-one nuclei have been reported in the literature for being important in the development of medicines due to their broad spectrum of activities. In this way, the structural knowledge of this class becomes relevant for obtaining new materials with desired biological properties. This study presents the structural elucidation of five halogenated dihydroquinolines, as well as the discussion about the effect on the molecular conformation of the type and position of halogen atom on aromatic rings. Compounds I and IV differ in halogen substitution on 2-phenyl ring, while compounds III and V differ in halogen substitution on the benzylidene ring. Moreover, compound II has a para-substituted 2-phenyl ring in their molecular structure. The crystal packing of all five molecules is mainly ruled by C-H⋯O and C-H···halogen interactions that form dimers and chains. The shift in position and the kind of the halogen in ring C shows a starring role in the conformation of the studied compounds, and the packaging of these compounds is more susceptible to variations when the halogen position changes.Electrophilic aromatic substitution produces edge-specific modifications to CVD graphene and graphene nanoplatelets that are suitable for specific attachment of biomolecules.European governments are struggling to regain economic strength in the coronavirus pandemic as in many countries the number of new infections seems to gradually subside. Growth rates deep in the red call for a reconstruction programme when the crisis is finally manageable and economic activity can resume. Amidst this, there are again influential groups that claim „this is not the time to insist on strict climate protection goals”. On the contrary, the ongoing COVID-19 crisis has clearly illustrated what climate disasters, often occurring locally, could do to the life of citizens. The reconstruction programme needs to initiate the great green transition. The transformation from a climate-distorting to a climate-protecting economy opens up investment opportunities and points to financing needs comparable with those necessary for the rebuilding of the European economy after World War II. The great green transition is a unique chance to pursue policies for a new and sustainable growth regime.The premise of this paper is that state aid to distressed companies should benefit not only the current owners but also the employees, who are the ones taking personal risks to continue or restart companies. Government aid during the Great Recession was aimed primarily at restoring the status quo. In the current deeper crisis, aid should be designed to create a fairer, more inclusive and more socially responsible economy by promoting employee ownership as both an incentive and a reward. We show how the Employee Stock Ownership Plan, which has been pioneered in the US for 40 years and can be adapted to the European legal context, can be used as the vehicle for structuring this aid.This paper discusses how the technical foundations of the EU’s fiscal rules constrain the fiscal space in EU countries in the context of the COVID-19 pandemic. We review the evidence on how estimates of potential output, which are at the heart of essential control indicators in EU fiscal surveillance, were revised in the ten years running up to the COVID-19 pandemic, and how these revisions affected the fiscal stance of EU countries. We provide first evidence for downward revisions in the European Commission’s potential output estimates against the background of the COVID-19 shock across the EU27 countries, and we assess the potential consequences in terms of fiscal space. According to our results, one additional percentage point in predicted losses of actual output is associated with a loss in potential output of about 0.6 percentage points. Given the importance of model-based estimates in the EU’s fiscal rules, avoiding pro-cyclical fiscal tightening will require that policymakers’ hands are not tied by overly pessimistic views on the development of potential output.Policymakers, experts and the general public heavily rely on the data that are being reported in the context of the coronavirus pandemic. Daily data releases on confirmed COVID-19 cases and deaths provide information on the course of the pandemic.While the COVID-19 pandemic posits a significant challenge to all societies around the world, it also reveals in the most dramatic manner the many abysmal differences between so-called advanced economies and the developing world.The long-term fiscal and economic damage of eurobonds in a rule-based fiscal architecture – as history corroborates – would be greater than the historical challenge of the coronavirus pandemic, unless there is a political union in Europe.With public debt-to-GDP levels now set to surpass post-war records and Italy’s ratio approaching levels reached in Greece on the eve of the country’s debt restructuring in early 2012, fears of a return of the sovereign debt crisis have emerged.Although austerity was particularly strong in the aftermath of the economic crisis of 2008 and its consequences in the euro area, Italian fiscal policies have been characterised by tough consolidation periods ever since the 1990s.Although the common perception is that the pandemic is 'the great equaliser’, workers’ tasks, contractual framework and position in the internal organisational hierarchy strongly affect their ability to work remotely.On 2 April 2020, the European Commission (2020) duly put forward a proposal for the creation of a European instrument for temporary Support to mitigate Unemployment Risks in an Emergency (SURE). This bold and innovative move must be welcome, but the actual profile of this new instrument requires clarification to avoid misunderstandings, false expectations and eventual disappointment.The COVID-19 pandemic carries heavy threats, and preserving stable and coordinated international trade relations will be essential to avoid catastrophic disorders or conflicts.Sooner or later, the ECB must accept that monetary financing in support of deficit spending is a necessity not just for mitigating the coronavirus crisis, but also for averting a downward deflationary cycle that could pull the eurozone apart.The relationship between working hours and sustainability has attracted research attention since at least the early 2000s, yet the role of care giving in this context is not well understood. Focusing on Australians between 40 and 60 years who have reduced their working hours and income, we explore the relationship between working hours, care giving and consumption. Data from the national census (Australian Bureau of Statistics, 2006, Australian Bureau of Statistics, 2011, Australian Bureau of Statistics, 2016c) were analysed to contextualise patterns in paid working hours, income and carer roles for men and women aged between 40 and 60 years. Findings from a national survey on informal carers (ABS, 2016a) were also consulted. Taken together, the two sources of national data showed that two thirds of all informal carers are women, that the likelihood of assuming informal carer roles increases with age, and that men and women in carer roles work fewer paid hours per week and have a lower weekly income than non-carers of the same age. To gain qualitative insights into these patterns in Australian national data, and the likely implications of carer roles for household consumption, semi-structured interviews were conducted with ten households who subsequently recorded details of their consumption-related expenses over a seven-day period. The interview data showed the strong connection between carer roles, reduced income and paid working hours and its strongly gendered dimension. We argue that women primarily 'downshift’ to undertake care rather than for sustainability motivations and that there is consequently a need to connect scholarship on gender and care with that on downshifting. The link between reducing paid working hours, care-giving and household consumption appeared to be less straight forward and varied between households. Our findings suggest that a complex relationship exists between environmental and social welfare concerns that has policy implications and warrants further exploration.Recent years have witnessed calls to 'unlock’ private capital and unleash a wave of green finance that can address the global environmental crisis. To this end, ample resources are being invested in the rapidly growing market for green bonds a debt security that links finance to projects that claim environmental benefits. This has placed green bonds in the vanguard of green finance, with a promise of treating our ecological deficit with debt. Such positioning demands close scrutiny of their obstacles, opportunities, and socio-environmental impacts. This paper contributes to this task with a multi-disciplinary review of green bond media articles, grey literature, and academic research. The paper has three key aims. It seeks to provide an introduction to green bonds for scholars who are not fluent in finance. Secondly, it attempts to provide a platform for further green finance research by delineating the major practical and political concerns with green bonds. Finally, it aims to widen our view of the green bond market by putting applied and critical research agendas into direct conversation. The paper concludes by calling for more explicit analysis of what green bonds can actually do; centring an expanded notion of greenwashing in green bond discourse; and pursuing more comparative, case driven research on green bond market development.Dimethoxymethane (DMM)-diesel blended fuels can simultaneously reduce exhaust emissions of soot and nitrogen oxide (NOX); several studies have been conducted in this regard. However, the influence of additive DMM on the production of inception and precursors of particulates, especially the relation between oxidation, morphology, and the nanostructure of soot particles has not been extensively investigated. In this study, a transmission electron microscope (TEM) and a thermogravimetric analyzer are introduced to acquire TEM images and conduct temperature-programmed-oxidation experiments. Aiming to study the influence of DMM addition on soot oxidation, morphology, and nanostructure, tests are conducted at different rotational speeds (1400 rpm and 2200 rpm), two engine loads (0.6 MPa and 1.2 MPa), and three fuels (D100, DMM6.4, and DMM13). The results show that the diameter distributions of all samples display a similar distribution, with the range of sample diameters being from 10 to 45 nm, and the addition of DMM reduces the dp (primary particle diameters) and the Df (fractal dimension), indicating a decreased structural compactness of aggregates, compared with diesel. Moreover, with increasing load and speed, La (the length of the fringe) increases and d (the distance between adjacent layer planes) decreases. Furthermore, with the addition of DMM, a more regular and higher degree of graphitization within soot particles can be observed in comparison to D100. The nanostructure influences the oxidation reaction of graphene segments with a line relation, leading to a difference in soot oxidation property.Rice (Oryza sativa L.) plants have the ability to develop ratoon tillers if the terminal growing point is lost, such as when the panicle has been aborted, matured, or harvested. We examined postharvest and midseason ratooning as management strategies for damaged rice crops, both in irrigated and rainfed conditions. Genotypic variation was observed in terms of postharvest ratoon tillering, midseason ratoon crop growth after lodging, and midseason ratoon crop growth after drought stress. The genotypic variation in postharvest ratoon tillering was related to stem carbohydrate levels at the time of main crop harvest and was affected by soil moisture levels at the time of main crop harvest. Drought-tolerant varieties did not consistently show improved ratoon crop growth. After lodging, cutting stems at a height of 30 cm produced the highest numbers of ratoon tillers, and the contribution of the ratoon crop to the total harvestable grain yield was highest when the ratoon crop was initiated at earlier growth stages. The highest ratoon grain yields recovered from lodged crops ranged up to 3.58 t ha-1. Total grain yield after drought was improved by trimming the leaves and panicles only in certain conditions and did not appear to be correlated with stem carbohydrate levels. These results suggest that management strategies may be recommended to farmers that exploit the ratooning ability of rice for improved recovery after midseason crop damage.Carabid beetles can greatly contribute to biocontrol in agroecosystems, reducing both insect pests and weed seeds. However, insect foraging and feeding behavior can be highly dependent on the interaction network and spatial structure of the environment, which can make their biocontrol contributions variable. In the present article, we explore how the interaction network of carabids can affect their behavior and how spatial vegetation structure and specific agronomy practices can, in turn, affect the strength of interactions in their network. We suggest that research on carabid biocontrol should move toward an approach in which the network of interactions among pests, carabids, and other organisms within its spatial structure is evaluated, with equal focus on direct and indirect interactions, and provide examples of tools to do so. Overall, we believe this approach will improve our knowledge of carabid networks, help to elucidate the underlying mechanisms of biocontrol, and lay the foundation for future biocontrol strategies.Background While current chemotherapy has increased cure rates for children with acute lymphoblastic leukaemia (ALL), the largest number of relapsing patients are still stratified as medium risk (MR) at diagnosis (50-60%). This highlights an opportunity to develop improved relapse-prediction models for MR patients. We hypothesised that bone marrow from MR patients who eventually relapsed would regrow faster in a patient-derived xenograft (PDX) model after induction chemotherapy than samples from patients in long-term remission. Methods Diagnostic bone marrow aspirates from 30 paediatric MR-ALL patients (19 who relapsed, 11 who experienced remission) were inoculated into immune-deficient (NSG) mice and subsequently treated with either control or an induction-type regimen of vincristine, dexamethasone, and L-asparaginase (VXL). Engraftment was monitored by enumeration of the proportion of human CD45+ cells (%huCD45+) in the murine peripheral blood, and events were defined a priori as the time to reach 1% huCD45+, 25% huCD45+ (TT25%) or clinical manifestations of leukaemia (TTL). Results The TT25% value significantly predicted MR patient relapse. Mutational profiles of PDXs matched their tumours of origin, with a clonal shift towards relapse observed in one set of VXL-treated PDXs. Conclusions In conclusion, establishing PDXs at diagnosis and subsequently applying chemotherapy has the potential to improve relapse prediction in paediatric MR-ALL.The expression of the CXCR4 chemokine receptor on CD34-positive blood cells is reduced in persons with primary myelofibrosis (PMF). We analyzed the relevance of cytofluorimetric assessment of the percentage of CD34-positive blood cells that had a positive CXCR4 surface expression (CD34/CXCR4-se) in a large cohort of subjects with myeloproliferative neoplasms. Mean CD34/CXCR4-se was lower in subjects with PMF compared with those with essential thrombocythemia (ET) or polycythemia vera (PV). A cutoff value of 39% was associated with a diagnosis of pre-fibrotic PMF vs. ET with a positive predictive value of 97%. In PMF male sex, older age, and MPL mutation were independent correlates of reduced CD34/CXCR4-se and associated with a briefer interval to development of severe anemia, large splenomegaly, thrombocytopenia, leukopenia, elevated CD34-positive blood cells, blast transformation and death. We constructed a prognostic model including age >65 years, hemoglobin 50 × 106/L, and CD34/CXCR4-se less then 39% at diagnosis. The model identified three risk cohorts with greater accuracy compared with the International Prognostic Scoring System. In conclusion, CD34/CXCR4-se is a highly sensitive marker of disease activity and a new potential diagnostic and prognostic biomarker in PMF.Insulin signaling is critical for neuroplasticity, cerebral metabolism as well as for systemic energy metabolism. In rodent studies, impaired brain insulin signaling with resultant insulin resistance (IR) modulates synaptic plasticity and the corresponding behavioral functions. Despite discoveries of central actions of insulin, in vivo molecular mechanisms of brain IR until recently have proven difficult to study in the human brain. In the current study, we leveraged recent technological advances in molecular biology and herein report an increased number of exosomes enriched for L1CAM, a marker predominantly expressed in the brain, in subjects with major depressive disorder (MDD) as compared with age- and sex-matched healthy controls (HC). We also report increased concentration of the insulin receptor substrate-1 (IRS-1) in L1CAM+ exosomes in subjects with MDD as compared with age- and sex-matched HC. We found a relationship between expression of IRS-1 in L1CAM+ exosomes and systemic IR as assessed by homeostatic model assessment of IR in HC, but not in subjects with MDD. The increased IRS-1 levels in L1CAM+ exosomes were greater in subjects with MDD and were associated with suicidality and anhedonia. Finally, our data suggested sex differences in serine-312 phosphorylation of IRS-1 in L1CAM+ exosomes in subjects with MDD. These findings provide a starting point for creating mechanistic framework of brain IR in further development of personalized medicine strategies to effectively treat MDD.An amendment to this paper has been published and can be accessed via a link at the top of the paper.Objective Gambling may cause a variety of problems, both health and social, to the player, his family and his environment; Problems can be more serious for those who gamble more frequently or bet more money. Beyond the mental health gambling disorder and considering other harms derived from gambling, it is possible to develop a public health approach to the issue, including both prevention and harm reduction aspects. In recent decades gambling availability has expanded, with attempts at regulation. The objective of this paper is to provide basic information about gambling in Spain, stratifying data by Autonomous Communities (AC), from a public health perspective. Methods A descriptive study of some aspects of gambling in Spain was carried out. The data for amounts gambled by participants, gross gaming revenue of the industry, and establishments or machines licensed for the year 2017 were extracted from the available systematic sources. Aggregated data were tabulated and stratified by AC for those presential gambling categories with the greatest compulsive gambling potential and relevant business volume.